In this work we propose a multimatcher system for fingerprint verification for obtaining a system that is almost comparable with the state-of-the-art commercial matchers. One of the main problems in fingerprint identification, given the frequency of low quality images, is to adjust, as optimally as possible, the alignment of two fingerprints for comparison. In this paper we describe a multimatcher system that varies the preprocessing method using different wavelet decompositions of the original fingerprint image. After the alignment step, we also propose some variants of the widely used TICO method that utilize different descriptors (minutiae-based, correlation-based, and texture-based methods) for describing the area around the minutiae. Moreover, since our alignment method is based on texture features describing the appearance of the fingerprint pattern in a broad region around the minutia, we find that we can also couple it with Biohashing for obtaining a more secure fingerprint authentication approach. Results are validated on all four FVC2004 DBs and on the easier FVC2002 DB2. We also investigate the fusion among the proposed methods with the competitor systems in FVC2004 competition. The MATLAB code used in our experiments is freely available for download at http://www.dei.unipd.it/wdyn/?Idsezione=3314&Idgruppo_pass=124.
L. Nanni, A. Lumini, S. Brahnam (2013). A secure multimatcher system for fingerprint verification.
A secure multimatcher system for fingerprint verification
LUMINI, ALESSANDRA;
2013
Abstract
In this work we propose a multimatcher system for fingerprint verification for obtaining a system that is almost comparable with the state-of-the-art commercial matchers. One of the main problems in fingerprint identification, given the frequency of low quality images, is to adjust, as optimally as possible, the alignment of two fingerprints for comparison. In this paper we describe a multimatcher system that varies the preprocessing method using different wavelet decompositions of the original fingerprint image. After the alignment step, we also propose some variants of the widely used TICO method that utilize different descriptors (minutiae-based, correlation-based, and texture-based methods) for describing the area around the minutiae. Moreover, since our alignment method is based on texture features describing the appearance of the fingerprint pattern in a broad region around the minutia, we find that we can also couple it with Biohashing for obtaining a more secure fingerprint authentication approach. Results are validated on all four FVC2004 DBs and on the easier FVC2002 DB2. We also investigate the fusion among the proposed methods with the competitor systems in FVC2004 competition. The MATLAB code used in our experiments is freely available for download at http://www.dei.unipd.it/wdyn/?Idsezione=3314&Idgruppo_pass=124.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.